AWS Machine Learning Blog

OCTOBER 6, 2023

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. Another essential component is an orchestration tool suitable for prompt engineering and managing different type of subtasks.

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ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. This trainable custom model can then be progressively improved through a feedback loop as shown above.

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IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. With multiple families in plan, the first release is the Slate family of models, which represent an encoder-only architecture. To bridge the tuning gap, watsonx.ai

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Unite.AI

JANUARY 23, 2024

Unlike traditional NLP models which rely on rules and annotations, LLMs like GPT-3 learn language skills in an unsupervised, self-supervised manner by predicting masked words in sentences. Their foundational nature allows them to be fine-tuned for a wide variety of downstream NLP tasks. This enables pretraining at scale.

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AWS Machine Learning Blog

MAY 7, 2024

This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain. Prompt engineering Prompt engineering enables you to instruct LLMs to generate suggestions, explanations, or completions of text in an interactive way.

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Prompt Engineer Prompt Engineering Large Language Models LLM 130

John Snow Labs

OCTOBER 16, 2023

The recent NLP Summit served as a vibrant platform for experts to delve into the many opportunities and also challenges presented by large language models (LLMs). At the recent NLP Summit, experts from academia and industry shared their insights. solves this problem by extracting metadata during the data preparation process.

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John Snow Labs

OCTOBER 12, 2023

We are thrilled to release NLP Lab 5.4 which brings a host of exciting enhancements to further empower your NLP journey. Publish Models Directly into Models HUB We’re excited to introduce a streamlined way to publish NLP models to the NLP Models HUB directly from NLP Lab.

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AWS Machine Learning Blog

DECEMBER 5, 2024

Used alongside other techniques such as prompt engineering, RAG, and contextual grounding checks, Automated Reasoning checks add a more rigorous and verifiable approach to enhancing the accuracy of LLM-generated outputs. These methods, though fast, didnt provide a strong correlation with human evaluators.

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AWS Machine Learning Blog

OCTOBER 24, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses ML to extract insights from text. For example, we can follow prompt engineering best practices to fine-tune an LLM to format dates into MM/DD/YYYY format, which may be compatible with a database DATE column.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

They have deep end-to-end ML and natural language processing (NLP) expertise and data science skills, and massive data labeler and editor teams. Strong domain knowledge for tuning, including prompt engineering, is required as well. Only prompt engineering is necessary for better results.

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Bugra Akyildiz

MARCH 16, 2024

Of course, all of the things that control vector does can be done through prompt engineering as well as one can consider control vector to be an “addition” to the prompt that is provided by the user. This is where metadata comes in. Metadata is essentially data about data.

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AWS Machine Learning Blog

JULY 24, 2024

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning.

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Heartbeat

SEPTEMBER 19, 2023

Comet’s LLMOps tool provides an intuitive and responsive view of our prompt history. Prompt Playground: With the LLMOps tool comes the new Prompt Playground, which allows Prompt Engineers to iterate quickly with different Prompt Templates and understand the impact on different contexts.

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ODSC - Open Data Science

JULY 12, 2024

The fields of AI and data science are changing rapidly and ODSC West 2024 is evolving to ensure we keep you at the forefront of the industry with our all-new tracks, AI Agents , What’s Next in AI, and AI in Robotics , and our updated tracks NLP, NLU, and NLG , and Multimodal and Deep Learning , and LLMs and RAG.

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Unite.AI

AUGUST 4, 2023

In this article, we will delve deeper into these issues, exploring the advanced techniques of prompt engineering with Langchain, offering clear explanations, practical examples, and step-by-step instructions on how to implement them. Prompts play a crucial role in steering the behavior of a model.

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ODSC - Open Data Science

JULY 26, 2024

You’ll also be introduced to prompt engineering, a crucial skill for optimizing AI interactions. You’ll explore data ingestion from multiple sources, preprocessing unstructured data into a normalized format that facilitates uniform chunking across various file types, and metadata extraction.

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Heartbeat

FEBRUARY 8, 2024

These components connect seamlessly, allowing you to build applications with less code and without requiring complicated natural language processing (NLP) tasks. LangChain loads training data in the form of ‘Documents’, and these contain text from various sources and their respective metadata.

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Heartbeat

JANUARY 26, 2024

In this experiment, I’ll use Comet LLM to record prompts, responses, and metadata for each memory type for performance optimization purposes. ") How the data is logged in Comet LLM: Input/ Output are log in Comet LLM (Image by the Author) Also the metadata in LLM 2. It seems to be a problem with the zipper.

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AWS Machine Learning Blog

APRIL 16, 2024

Text to SQL: Using natural language to enhance query authoring SQL is a complex language that requires an understanding of databases, tables, syntaxes, and metadata. The way you craft a prompt can profoundly influence the nature and usefulness of the AI’s response. In his free time, he enjoys playing chess and traveling.

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Snorkel AI

MARCH 2, 2023

As we look at the progression, we see that these state-of-the-art NLP models are getting larger and larger over time. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities. Then comes prompt engineering.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

As we look at the progression, we see that these state-of-the-art NLP models are getting larger and larger over time. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities. Then comes prompt engineering.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Heartbeat

DECEMBER 15, 2023

Moreover, the NewsURLLoader can perform light NLP (Natural Language Processing) tasks. NLP Enhancements : The optional NLP features of the NewsURLLoader add an extra layer of value. It offers clean, concise, and relevant text extraction, with the bonus of NLP processing.

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AI Chatbots LLM Chatbots NLP 52

AWS Machine Learning Blog

AUGUST 30, 2024

Prompt engineering best practices for Meta Llama 3 The following are best practices for prompt engineering for Meta Llama 3: Base model usage – Base models offer the following: Prompt-less flexibility – Base models in Meta Llama 3 excel in continuing sequences and handling zero-shot or few-shot tasks without requiring specific prompt formats.

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John Snow Labs

MAY 21, 2024

We are happy to announce the release of Generative AI Lab, marking the transition from the previous NLP Lab to a state-of-the-art No-Code platform that enables domain experts to train task-specific AI models using large language models (LLMs). Finally, the playground allows easy editing and testing of prompts, rules, or models without coding.

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Bugra Akyildiz

JULY 4, 2024

Apply NLP techniques to analyze unstructured log data and extract meaningful insights. Metadata, callbacks, and data format conversions. This can help in early detection of issues before they impact users. Use historical telemetry data to predict potential issues or performance degradation.

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AWS Machine Learning Blog

DECEMBER 4, 2024

The core challenge lies in developing data pipelines that can handle diverse data sources, the multitude of data entities in each data source, their metadata and access control information, while maintaining accuracy. As a result, they can index one time and reuse that indexed content across use cases.

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